Hematopoietic stem cell transplantation (HSCT) is a critical treatment for hematologic malignancies such as leukemia and lymphoma but carries significant risks, including graft-versus-host disease (GVHD), relapse, and treatment-related mortality. The growing volume of clinical, genomic, and biomarker data has led to increased interest in machine learning (ML) as a tool to enhance decision-making and improve patient outcomes.
A study explores the applications of ML in HSCT, particularly in donor selection, conditioning regimens, and post-transplant outcome prediction. Various ML models, including decision trees, random forests, and neural networks, have been utilized to refine donor compatibility algorithms, predict relapse and mortality risks, and assess GVHD susceptibility.
Additionally, the integration of “omics” data with ML has facilitated the identification of novel biomarkers, supporting more personalized treatment approaches. ML models are clinically validated and can be integrated into existing workflows.
Early applications of machine learning in transplantation medicine are promising. As AI continues to evolve, its role in regenerative medicine will likely expand, leading to more personalized and effective treatment strategies for patients undergoing stem cell transplants.
